With different choices of order \(q\) , (REF ) leads to
various specific forms of the \(l_{q}\)  regularizer. In particular,
when \(q=2\) , \(f_{{\bf z},\lambda ,q}\)  corresponds to the ridge
regressor [1]}, which smoothly shrinks the
coefficients toward zero. When \(q=1\) , \(f_{{\bf z},\lambda ,q}\)  leads
to the LASSO [2]}, which set small coefficients
exactly at zero and thereby also serves as a variable selection
operator. When \(0< q <1\) , \(f_{{\bf z},\lambda ,q}\)  coincides with the
bridge estimator [3]}, which tends to produce
highly sparse estimates through a non-continuous shrinkage.
